CN116011153A - Power distribution network reliability assessment method based on light-load correlation enhanced reliability - Google Patents
Power distribution network reliability assessment method based on light-load correlation enhanced reliability Download PDFInfo
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Abstract
The invention relates to a power distribution network reliability evaluation method based on light-load correlation enhancement reliability, which belongs to the technical field of power distribution network reliability evaluation, and comprises the following steps: inputting the reliability original data of the power distribution network; establishing a light-load joint probability distribution function according to historical photovoltaic and load power data; generating an optical-charge typical scene considering time sequence correlation according to the established optical-charge joint probability distribution function; constructing a reliability evaluation model of the power distribution network; and researching a reliability operation strategy after the distributed photovoltaic and energy storage are accessed into the power distribution network based on the light-load typical scene and the power distribution network reliability evaluation model to evaluate the reliability of the power distribution network. The method fully considers the time sequence correlation between the regional source and the load, performs the reliability evaluation of the power distribution network through the generated light-load combined time sequence scene, has higher reliability than the conventional reliability evaluation result in the calculated power distribution network reliability index, and is beneficial to the subsequent operation and planning design of the power distribution network.
Description
Technical Field
The invention belongs to the technical field of reliability evaluation of power distribution networks, and particularly relates to a power distribution network reliability evaluation method based on light-load correlation enhancement reliability.
Background
Reliable power supply is the most basic guarantee of national economic development, and the distribution network is positioned at the extreme end of the power system and is a part of the whole power system directly connected with users, so that the heavy duty of high-reliability power supply is born. After the distribution network is connected with the distributed roof photovoltaic with volatility and randomness, the uncertainty characteristic between the source and the load is further increased, and a great challenge is brought to high-reliability power supply of the distribution network.
For reliability evaluation of distributed energy sources such as roof photovoltaics after being connected into a power distribution network, the most important is how to model the distributed photovoltaics with random output. Currently, methods for modeling distributed photovoltaic output can be roughly classified into 2 kinds of analytical methods and simulation methods. The analytical method is mainly to establish a distributed photovoltaic output model by a method for counting photovoltaic output probability, and the simulation method can directly adopt annual photovoltaic output data in a history area, can model light intensity by beta distribution and then generate an annual photovoltaic output curve based on the corresponding relation between photovoltaic output and light intensity. For example, a learner introduces a point estimation method to transform the probability problem of distributed photovoltaic and load power into a deterministic mathematical model for reliability assessment; the power distribution network reliability evaluation method based on the distributed energy source has the advantages that students adopt beta distribution to simulate light intensity fluctuation in a power distribution network reliability evaluation flow containing the distributed energy source, normal distribution is adopted to simulate load fluctuation, and the fluctuation of power on the source side and the load side is respectively simulated in a probability distribution mode.
The current research only realizes probability density modeling on photovoltaic and load power respectively through a parameter or non-parameter estimation method at most, and then carries out distribution network reliability assessment, and correlation between photovoltaic and load (source-load) power in the same area is not considered yet. However, the distributed photovoltaic output and load power at the moment of occurrence of the power distribution network fault have a decisive effect on how large the island operation area of the power distribution network can be supported by the mutual cooperation of the distributed photovoltaic and the energy storage device, and if the difference between the source-load matching power and the actual situation is too large, the reliability of the operation reliability evaluation of the power distribution network can be reduced. Therefore, there is a need to develop a method for enhancing the reliability of the power distribution network based on the correlation between light and load.
Disclosure of Invention
The invention aims to provide a reliability evaluation method of a power distribution network based on light-load correlation enhancement reliability, which is used for solving the technical problems in the prior art, and the current research only aims at modeling probability density of photovoltaic power and load power respectively by a parameter or non-parameter estimation method and then evaluating the reliability of the power distribution network, and does not consider the correlation between the photovoltaic power and the load (source-load) power in the same area. However, the distributed photovoltaic output and load power at the moment of occurrence of the power distribution network fault have a decisive effect on how large the island operation area of the power distribution network can be supported by the mutual cooperation of the distributed photovoltaic and the energy storage device, and if the difference between the source-load matching power and the actual situation is too large, the reliability of the operation reliability evaluation of the power distribution network can be reduced.
In order to achieve the above purpose, the technical scheme of the invention is as follows:
a power distribution network reliability assessment method based on light-load correlation enhancement assessment reliability comprises the following steps:
s1, inputting original reliability data of a power distribution network;
s2, establishing a light-load joint probability distribution function according to historical photovoltaic and load power data;
s3, generating an optical-charge typical scene considering correlation according to the optical-charge joint probability distribution function established in the step S2;
s4, constructing a reliability evaluation model of the power distribution network;
s5, researching a reliability operation strategy after the distributed photovoltaic and energy storage are accessed into the power distribution network based on the light-load typical scene in the step S3 and the power distribution network reliability evaluation model in the step S4 to evaluate the reliability of the power distribution network.
Further, the method for evaluating the reliability of the power distribution network based on the enhancement of the light-load correlation is characterized in that the step S1 includes: the input power distribution network reliability original data comprise various data of load points, switch positions, transformers, line types and lengths, distributed photovoltaics, reliability parameters of energy storage devices, access positions and configuration conditions of the distributed photovoltaics and the energy storage devices and a distributed photovoltaics and load historical output curve.
Further, the method for evaluating the reliability of the power distribution network based on the enhancement of the light-load correlation is characterized in that the step S2 comprises the following steps: the method comprises the steps of establishing a light-load joint probability distribution function according to historical photovoltaic and load power data, wherein the light-load joint probability distribution function comprises the following specific steps:
dividing collected historical photovoltaic and load power data into n samples taking a day as a unit, wherein each sample contains twenty-four hours of time sequence power output conditions, respectively carrying out non-parametric density estimation on each time period to obtain a probability density function of photovoltaic and load power of each hour within twenty-four hours, and then establishing an optical-load power joint probability distribution function of twenty-four time periods through a Copula function, wherein the mathematical expression is as follows:
wherein:
u x ＝(x-X i )/h x
u y ＝(y-Y i )/h y
wherein: f (x), f (y) represent probability density functions of distributed photovoltaic, load power; x is X i 、Y i The power actual value of the distributed photovoltaic and load; x and y are independent variables of the photovoltaic and load corresponding to the nuclear density estimation; n is n x 、n y For sample capacity; h is a x 、h y Is the bandwidth; k (K) x (·)、K y (. Cndot.) is a kernel function corresponding to the photovoltaic and the load, and the Gaussian function is selected in the patent; t represents 24 time periods a day; f (F) T (x T ,y T ) Representing a joint probability distribution function of the T-period distributed photovoltaic and the load;
representing an edge distribution function of the distributed photovoltaic output in a period T;
Representing the edge distribution function of the load power over the period T.
Further, the method for evaluating the reliability of the power distribution network based on the enhancement of the light-load correlation is characterized in that the step S3 includes: generating an optical-charge typical scene considering correlation according to the optical-charge joint probability distribution function established in the step S2, wherein the optical-charge typical scene specifically comprises the following steps:
generating 24 random numbers u with values ranging from 0 to 1 based on Monte Carlo sampling idea 1 ，u 2 ，…，u n Namely, the edge distribution function value of the photovoltaic power in 24 time periods is obtained, and the edge distribution function v of the load output in 24 time periods is solved according to the joint probability distribution function of the photovoltaic power and the load in 24 time periods calculated by adopting the Copula function in the step S3 1 ，v 2 ，…，v n The operation is repeated for K times to generate K groups of edge distribution function values of the photovoltaic-load, then real output values of the photovoltaic-load considering time sequence correlation are obtained through respective inverse function calculation, then the scene generated by K groups of Monte Carlo sampling is clustered based on a fuzzy C-means clustering algorithm, the clustering is performed according to the processes of initializing the central category of the clustering, giving membership factor fuzzification parameters, initializing membership matrix, giving iteration termination criteria, calculating clustering centers, updating membership matrix, judging whether the termination criteria are met or not and outputting clustering results, and finally K typical photovoltaic-load output daily scenes (K can be selected according to the seasonal characteristics of the distributed photovoltaic access area affecting light-load output) are obtained, wherein the mathematical expression is as follows:
wherein, C (&) represents a joint probability distribution function of photovoltaic and load; t represents 24 hours a day; u (u) T An edge distribution function value representing a photovoltaic power T period; v T An edge distribution function value representing a load power T period; x is x T Representing the true output value of the photovoltaic at the moment T; y is T Representing the real power value of the load at the moment T;
an inverse function representing an edge distribution function of the photovoltaic power over a period T;
An inverse function representing an edge distribution function of the load power over a period T;
Is an FCM cluster center; i represents the i-th sample; n is the number of samples; j represents a j-th cluster center; k is the number of clustering centers; t is the number of iterations; u (u) ij Membership of the ith sample to the jth cluster center; x is x i Is a photovoltaic and load sample point; m is a membership factor parameter; d, d ij Is the Euclidean distance of the ith sample to the center of the jth cluster.
Further, the method for evaluating the reliability of the power distribution network based on the enhancement of the light-load correlation is characterized in that the step S4 includes: and constructing a reliability evaluation model of the whole power distribution network through reliability modeling of the photovoltaic element, reliability modeling of the energy storage element, reliability evaluation index selection of the power distribution network and reliability method research of the power distribution network.
Further, the reliability evaluation method of the power distribution network based on the light-load correlation enhancement evaluation reliability is characterized in that the reliability output of the photovoltaic element can be directly obtained by calculating the running state of the photovoltaic equipment by combining the distributed photovoltaic and load typical scene curve generated in the step S3, and the reliability modeling mathematical expression of the photovoltaic element is as follows:
P T′ ＝P T *PVOS t
wherein: p (P) T' Representing a distributed photovoltaic element reliability output value at time t after combining a power curve of the distributed photovoltaic with a reliable operating state of the distributed photovoltaic component element; p (P) T The distributed photovoltaic output value is read by the distributed photovoltaic and load typical scene curve generated in the step S3; PVOS (PVOS) t Representing the reliability comprehensive operation state of distributed photovoltaic component elements (such as an inverter, a photovoltaic panel and other equipment) at the time t;
the reliability modeling mathematical expression of the energy storage element is as follows:
SOCmin≤SOC(t)≤SOCmax
SOC(0)＝SOC(T)
wherein: SOC (t) is the stored energy state of charge at the current moment; SOC (State of Charge) max 、SOC min The upper limit and the lower limit of the energy storage charge state are respectively; η (eta) cha 、η dis The energy conversion efficiency during the charge and discharge of the stored energy is respectively; BOS (t) represents the running state of the energy storage device at the moment t; Δt takes one hour; x is x dis 、x cha The method comprises the steps of restraining the charging state and the discharging state of an energy storage device to be not carried out simultaneously for the charging and discharging states of the energy storage device; p (P) max And Q is equal to max Respectively representing the rated power and rated capacity of the energy storage system.
Further, the reliability evaluation method of the power distribution network based on the light-load time sequence correlation enhancement evaluation reliability is characterized in that the reliability evaluation index of the power distribution network selects average power supply availability of a system, expected value of electric quantity deficiency and average power supply quantity in an island state to evaluate the reliability of the whole power distribution network and verify the reliability of the power distribution network based on the light-load time sequence correlation generation of distributed photovoltaic and load scenes, and the mathematical expression is as follows:
wherein:
wherein: ASAI represents the average power availability of the system; EENS represents a low power desired value; IIES represents the average supply power R in the island state as the system load point set; u (U) i The duration of the annual fault for the load point; n (N) i The number of users at the load point i;
user average power representing load point i;
Representing the distributed photovoltaic average power accessed by the load point i;
representing the average power of the energy storage device accessed by the load point i; n represents the total number of island formations in a power distribution network in one year; n represents the formation of island for the nth time; r is R j Representing the island run formed the nth timeA range; Δt takes 1 hour; t is t n Indicating the island run time of the nth formation. />
Further, the method for evaluating the reliability of the power distribution network based on the enhancement of the light-load correlation is characterized in that the step S5 includes: based on the light-load typical scene in the step S3 and the reliability evaluation model of the power distribution network in the step S4, researching a reliability operation strategy after the distributed photovoltaic and energy storage are accessed into the power distribution network to evaluate the reliability of the power distribution network, wherein the reliability operation strategy is specifically as follows:
the energy storage device operation strategy in the reliability operation strategy operates in a mode of supplying power to a load according to a normal time low-storage high-discharge mode and a fault time island operation mode, in a normal operation mode, the patent adopts the operation strategy that the energy storage device discharges when the electricity price is at a highest value, charges when the electricity price is at a lowest value, and does not charge or discharge when the price is moderate to simulate the SOC operation curve of the energy storage system all the day, and the state of charge (SOC) of the energy storage device is assumed to be the maximum value of SOC at 6 am every day max Point 22 is the state of charge minimum value SOC of the energy storage device min The energy storage device is charged and discharged uniformly in the running process, and the piecewise function of the SOC of the energy storage system at each moment can be obtained as follows:
after the SOC curve of the energy storage system in normal operation is obtained, the probability corresponding to each SOC stage can be calculated, and when the power distribution network fails, the SOC value of the energy storage system at the failure moment can be obtained by sampling by the formula;
in view of the characteristic that a fault influence range in the power distribution network takes a switch as a boundary, the power distribution network fault partition strategy in the reliability operation strategy calculates the reliability by adopting a method of partitioning the power distribution network by taking the switch as a boundary, the reliability of load points in the partition is the same by the influence degree of faults of different elements outside the partition, so that the calculation amount of a sampling method can be simplified, when the power distribution network element breaks down, the whole feeder line is divided into a fault area, a fault upstream area, a fault downstream area and a fault non-influence area by adopting a fault partition strategy, and the power failure time of the load points of the fault area is the fault repair time of the element; the fault can be isolated through the switch, and the power failure time of the load point of the upstream region of the fault is the switching time of the switch; if an energy storage device and the like are arranged in the fault downstream area as a backup power supply, calculating the power failure time according to the energy storage configuration and the island operation strategy of the energy storage system; if no energy storage device is used as a backup power supply, the power failure time of a load point of a downstream area is the repair time of a fault element;
the island operation strategy in the reliability operation strategy takes energy storage and distributed photovoltaic as centers, and the island formation size is judged by combining four factors of an energy storage SOC sampling value, energy storage maximum charge and discharge power, distributed photovoltaic output and total load power in an island range in a fault period. If the energy storage device and the distributed photovoltaic device can ensure that the current island runs smoothly, the island range is enlarged to the periphery by taking the switch as a boundary until the island with the maximum range is formed, and when the combined output of the distributed photovoltaic device and the energy storage device is insufficient during the island duration, the load needs to be reduced, and the mathematical expression is as follows:
the constraint conditions are as follows:
wherein: x (m) is a criterion of whether an mth load point in the island is cut down, wherein the load point m is equal to 1 when the load point m is cut down, and is equal to 0 after the load point m is cut down; p (P) i dmax (t) is the maximum output value of the ith distributed energy storage device in the island at the moment t, N b The number of the distributed energy storage devices in the island; p (P) j DG (t) is the power value of the jth distributed photovoltaic in the island at the moment t, N DG The number of distributed photovoltaic devices in the island; l (L) t m (t) is the m < th > in islandActual power at each load point; m is the number of load points contained within the island; t is t 1 、t 2 The forming time of the island and the ending time of the island in the power distribution network fault period are respectively.
Further, the method for evaluating the reliability of the power distribution network based on the enhancement of the light-load correlation is characterized in that the step S5 includes: based on the light-load typical scene in the step S3 and the reliability evaluation model of the power distribution network in the step S4, the reliability evaluation of the power distribution network is carried out by researching a reliability operation strategy after the distributed photovoltaic and energy storage are accessed into the power distribution network, wherein the reliability evaluation flow comprises the following steps:
step1: reading in system original data and setting a simulation time axis;
step2: generating N0-1 random numbers, sampling states of all elements according to a sequential Monte Carlo simulation method, and calculating a fault-free running time TTF;
step3: obtaining a fault element corresponding to the minimum TTF, and calculating the fault repair time TTR according to the fault repair rate of the element;
step4: determining the island operation range based on the light-load typical scene generated in the step S3 and the power distribution network reliability evaluation model in the step S4;
step5: recording information such as power failure times, time and corresponding load power at power failure time of each load point in the power distribution network;
step6: updating a time axis, judging whether the set simulation time length is reached, and if the set simulation time length is not reached, jumping to Step2;
step7: and calculating the reliability index of the load point and the system by using the recorded information such as the power failure times, time and corresponding load power of the power failure time of the load point.
Compared with the prior art, the invention has the following beneficial effects:
the invention fully considers the time sequence correlation between the photovoltaic and the load (source-load) power in the same area, determines the island operation range based on the distributed photovoltaic and the load power curve generated by adopting the light-load time sequence correlation at the time of power distribution network faults, reduces the gap between the source-load power and the real situation, and improves the reliability of the power distribution network reliability assessment.
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FIG. 1 is a schematic flow chart provided by the present invention;
FIG. 2 is a schematic flow chart for establishing an optical-charge joint probability distribution function;
FIG. 3 is a flow diagram of generating an exemplary scenario of light-load considering correlation;
fig. 4 is a schematic flow chart of power distribution network reliability evaluation based on a light-load typical scene and a power distribution network reliability evaluation model.
Detailed Description
For the purpose of making the technical solution and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings and examples. It should be understood that the particular embodiments described herein are illustrative only and are not intended to limit the invention, i.e., the embodiments described are merely some, but not all, of the embodiments of the invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present invention. It is noted that relational terms such as "first" and "second", and the like, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
Examples:
as shown in fig. 1, a method for evaluating reliability of a power distribution network based on optical-load correlation enhancement evaluation reliability includes the following steps:
s1, inputting original reliability data of a power distribution network, wherein the original reliability data specifically comprises the following steps:
the input power distribution network reliability original data comprise various data of input load points, switch positions, transformers, line types and lengths, reliability parameters of distributed photovoltaic and energy storage devices, access positions and configuration conditions of the distributed photovoltaic and the energy storage devices and a distributed photovoltaic and load historical output curve.
S2, establishing a light-load joint probability distribution function according to historical photovoltaic and load power data, wherein the light-load joint probability distribution function specifically comprises the following steps:
as shown in fig. 2, the collected historical photovoltaic and load power data are divided into n samples with a day as a unit, each sample contains twenty-four hours of time sequence power output conditions, non-parametric density estimation is performed on each time period to obtain probability density functions of photovoltaic and load power of each time period within twenty-four hours, and then a light-load power joint probability distribution function of twenty-four time periods is established through a Copula function, wherein the mathematical expression is as follows:
wherein:
u x ＝(x-X i )/h x
u y ＝(y-Y i )/h y
wherein: f (x), f (y) represent probability density functions of distributed photovoltaic, load power; x is X i 、Y i The power actual value of the distributed photovoltaic and load; x and y are independent variables of the photovoltaic and load corresponding to the nuclear density estimation; n is n x 、n y For sample capacity; h is a x 、h y Is the bandwidth; k (K) x (·)、K y (. Cndot.) is a kernel function corresponding to the photovoltaic and the load, and the Gaussian function is selected in the patent; t represents 24 time periods a day; f (F) T (x T ,y T ) Representing a joint probability distribution function of the T-period distributed photovoltaic and the load;
representing an edge distribution function of the distributed photovoltaic output in a period T;
Representing the edge distribution function of the load power over the period T.
S3, generating an optical-charge typical scene considering correlation according to the optical-charge joint probability distribution function established in the step S2, wherein the optical-charge typical scene specifically comprises the following steps:
as shown in fig. 3, 24 random numbers u with values ranging from 0 to 1 are generated based on Monte Carlo sampling idea 1 ，u 2 ，…，u n Namely, the edge distribution function value of the photovoltaic power in 24 time periods is obtained, and the edge distribution function v of the load output in 24 time periods is solved according to the joint probability distribution function of the photovoltaic power and the load in 24 time periods calculated by adopting the Copula function in the step S3 1 ，v 2 ，…，v n The operation is repeated for K times to generate K groups of edge distribution function values of the photovoltaic-load, then real output values of the photovoltaic-load considering time sequence correlation are obtained through respective inverse function calculation, then the scene generated by K groups of Monte Carlo sampling is clustered based on a fuzzy C-means clustering algorithm, the clustering is performed according to the processes of initializing the central category of the clustering, giving membership factor fuzzification parameters, initializing membership matrix, giving iteration termination criteria, calculating clustering centers, updating membership matrix, judging whether the termination criteria are met or not and outputting clustering results, and finally K typical photovoltaic-load output daily scenes (K can be selected according to the seasonal characteristics of the distributed photovoltaic access area affecting light-load output) are obtained, wherein the mathematical expression is as follows:
wherein, C (&) represents a joint probability distribution function of photovoltaic and load; t represents 24 hours a day; u (u) T An edge distribution function value representing a photovoltaic power T period; v T An edge distribution function value representing a load power T period; x is x T Representing the true output value of the photovoltaic at the moment T; y is T Representing the real power value of the load at the moment T;
an inverse function representing an edge distribution function of the photovoltaic power over a period T;
An inverse function representing an edge distribution function of the load power over a period T;
Is an FCM cluster center; i represents the i-th sample; n is the number of samples; j represents a j-th cluster center; k is the number of clustering centers; t is the number of iterations; u (u) ij Membership of the ith sample to the jth cluster center; x is x i Is a photovoltaic and load sample point; m is a membership factor parameter; d, d ij Is the Euclidean distance of the ith sample to the center of the jth cluster.
S4, constructing a reliability evaluation model of the power distribution network, which specifically comprises the following steps:
and constructing a reliability evaluation model of the whole power distribution network through reliability modeling of the photovoltaic element, reliability modeling of the energy storage element, reliability evaluation index selection of the power distribution network and reliability method research of the power distribution network.
The reliability output of the photovoltaic element can be directly obtained by calculating the running state of the photovoltaic equipment by combining the distributed photovoltaic and load typical scene curve generated in the step S3, and the reliability modeling mathematical expression of the photovoltaic element is as follows:
P T′ ＝P T *PVOS t
wherein: p (P) T' Representing a distributed photovoltaic element reliability output value at time t after combining a power curve of the distributed photovoltaic with a reliable operating state of the distributed photovoltaic component element; p (P) T The distributed photovoltaic output value is read by the distributed photovoltaic and load typical scene curve generated in the step S3; PVOS (PVOS) t Representing the reliability comprehensive operation state of distributed photovoltaic component elements (such as an inverter, a photovoltaic panel and other equipment) at the time t;
the reliability modeling mathematical expression of the energy storage element is as follows:
SOCmin≤SOC(t)≤SOCmax
SOC(0)＝SOC(T)
wherein: SOC (t) is the stored energy state of charge at the current moment; SOC (State of Charge) max 、SOC min The upper limit and the lower limit of the energy storage charge state are respectively; η (eta) cha 、η dis The energy conversion efficiency during the charge and discharge of the stored energy is respectively; BOS (t) represents the running state of the energy storage device at the moment t; Δt takes one hour; x is x dis 、x cha The method comprises the steps of restraining the charging state and the discharging state of an energy storage device to be not carried out simultaneously for the charging and discharging states of the energy storage device; p (P) max And Q is equal to max Respectively representing the rated power and rated capacity of the energy storage system.
The reliability evaluation index of the power distribution network selects the average power supply availability of the system, the expected value of insufficient electric quantity and the average power supply quantity in an island state to evaluate the reliability of the whole power distribution network, and verifies the reliability of the power distribution network reliability evaluation can be improved by generating distributed photovoltaic and load scenes based on the light-load time sequence correlation, wherein the mathematical expression is as follows:
wherein:
wherein: ASAI represents the average power availability of the system; EENS represents a low power desired value; IIES represents the average supply power R in the island state as the system load point set; u (U) i The duration of the annual fault for the load point; n (N) i The number of users at the load point i;
user average power representing load point i;
Representing the distributed photovoltaic average power accessed by the load point i;
Representing the average power of the energy storage device accessed by the load point i; n represents the total number of island formations in a power distribution network in one year; n represents the formation of island for the nth time; r is R j Representing an island operation range formed for the nth time; Δt takes 1 hour; t is t n Indicating the island run time of the nth formation.
S5, carrying out power distribution network reliability evaluation based on the light-load typical scene in the step S3 and the power distribution network reliability evaluation model in the step S4, wherein the power distribution network reliability evaluation method specifically comprises the following steps:
the energy storage device operation strategy in the reliability operation strategy operates in a mode of supplying power to a load according to a normal time low-storage high-discharge mode and a fault time island operation mode, in a normal operation mode, the patent adopts the operation strategy that the energy storage device discharges when the electricity price is at a highest value, charges when the electricity price is at a lowest value, and does not charge or discharge when the price is moderate to simulate the SOC operation curve of the energy storage system all the day, and the state of charge (SOC) of the energy storage device is assumed to be the maximum value of SOC at 6 am every day max Point 22 is the state of charge minimum value SOC of the energy storage device min The energy storage device is charged and discharged uniformly in the running processThe piecewise function of the SOC at each moment of the energy storage system can be obtained as follows:
after the SOC curve of the energy storage system in normal operation is obtained, the probability corresponding to each SOC stage can be calculated, and when the power distribution network fails, the SOC value of the energy storage system at the failure moment can be obtained by sampling by the formula;
in view of the characteristic that a fault influence range in the power distribution network takes a switch as a boundary, the power distribution network fault partition strategy in the reliability operation strategy calculates the reliability by adopting a method of partitioning the power distribution network by taking the switch as a boundary, the reliability of load points in the partition is the same by the influence degree of faults of different elements outside the partition, so that the calculation amount of a sampling method can be simplified, when the power distribution network element breaks down, the whole feeder line is divided into a fault area, a fault upstream area, a fault downstream area and a fault non-influence area by adopting a fault partition strategy, and the power failure time of the load points of the fault area is the fault repair time of the element; the fault can be isolated through the switch, and the power failure time of the load point of the upstream region of the fault is the switching time of the switch; if an energy storage device and the like are arranged in the fault downstream area as a backup power supply, calculating the power failure time according to the energy storage configuration and the island operation strategy of the energy storage system; if no energy storage device is used as a backup power supply, the power failure time of a load point of a downstream area is the repair time of a fault element;
the island operation strategy in the reliability operation strategy takes energy storage and distributed photovoltaic as centers, and the island formation size is judged by combining four factors of an energy storage SOC sampling value, energy storage maximum charge and discharge power, distributed photovoltaic output and total load power in an island range in a fault period. If the energy storage device and the distributed photovoltaic device can ensure that the current island runs smoothly, the island range is enlarged to the periphery by taking the switch as a boundary until the island with the maximum range is formed, and when the combined output of the distributed photovoltaic device and the energy storage device is insufficient during the island duration, the load needs to be reduced, and the mathematical expression is as follows:
the constraint conditions are as follows:
wherein: x (m) is a criterion of whether an mth load point in the island is cut down, wherein the load point m is equal to 1 when the load point m is cut down, and is equal to 0 after the load point m is cut down; p (P) i dmax (t) is the maximum output value of the ith distributed energy storage device in the island at the moment t, N b The number of the distributed energy storage devices in the island; p (P) j DG (t) is the power value of the jth distributed photovoltaic in the island at the moment t, N DG The number of distributed photovoltaic devices in the island; l (L) t m (t) is the actual power at the mth load point in the island; m is the number of load points contained within the island; t is t 1 、t 2 The forming time of the island and the ending time of the island in the power distribution network fault period are respectively.
As shown in fig. 4, a specific flow of performing reliability evaluation of the power distribution network based on the light-load typical scene in step S3 and the reliability evaluation model of the power distribution network in step S4 to research the reliability operation policy after the distributed photovoltaic and energy storage are accessed into the power distribution network is as follows:
step1: reading in system original data and setting a simulation time axis;
step2: generating N0-1 random numbers, sampling states of all elements according to a sequential Monte Carlo simulation method, and calculating a fault-free running time TTF;
step3: obtaining a fault element corresponding to the minimum TTF, and calculating the fault repair time TTR according to the fault repair rate of the element;
step4: determining island operation range size based on the light-load typical scene generated in the step S3 and the power distribution network reliability evaluation model in the step S4
Step5: recording information such as power failure times, time and corresponding load power at power failure time of each load point in the power distribution network;
step6: updating a time axis, judging whether the set simulation time length is reached, and if the set simulation time length is not reached, jumping to Step2;
step7: and calculating the reliability index of the load point and the system by using the recorded information such as the power failure times, time and corresponding load power of the power failure time of the load point.
The above is a preferred embodiment of the present invention, and all changes made according to the technical solution of the present invention belong to the protection scope of the present invention when the generated functional effects do not exceed the scope of the technical solution of the present invention.
Claims (9)
1. The power distribution network reliability evaluation method based on the light-load correlation enhancement reliability is characterized by comprising the following steps of:
s1, inputting original reliability data of a power distribution network;
s2, establishing a light-load joint probability distribution function according to historical photovoltaic and load power data;
s3, generating an optical-charge typical scene considering correlation according to the optical-charge joint probability distribution function established in the step S2;
s4, constructing a reliability evaluation model of the power distribution network;
s5, researching a reliability operation strategy after the distributed photovoltaic and energy storage are accessed into the power distribution network based on the light-load typical scene in the step S3 and the power distribution network reliability evaluation model in the step S4 to evaluate the reliability of the power distribution network.
2. The method for evaluating reliability of a power distribution network based on optical-load correlation enhancement reliability according to claim 1, wherein in step S1, the input power distribution network reliability raw data includes various data of load points, switch positions, transformers, line types and lengths, reliability parameters of distributed photovoltaic and energy storage devices, access positions and configuration conditions of the distributed photovoltaic and energy storage devices, and a distributed photovoltaic and load historical output curve.
3. The method for evaluating reliability of a power distribution network based on optical-load correlation enhancement reliability according to claim 2, wherein in step S2: the method comprises the steps of establishing a light-load joint probability distribution function according to historical photovoltaic and load power data, wherein the light-load joint probability distribution function comprises the following specific steps:
dividing collected historical photovoltaic and load power data into n samples taking a day as a unit, wherein each sample contains twenty-four hours of time sequence power output conditions, respectively carrying out non-parametric density estimation on each time period to obtain a probability density function of photovoltaic and load power of each hour within twenty-four hours, and then establishing an optical-load power joint probability distribution function of twenty-four time periods through a Copula function, wherein the mathematical expression is as follows:
wherein:
u x ＝(x-X i )/h x
u y ＝(y-Y i )/h y
wherein: f (x), f (y) represent distributed photovoltaic, loadProbability density function of power; x is X i 、Y i The power actual value of the distributed photovoltaic and load; x and y are independent variables of the photovoltaic and load corresponding to the nuclear density estimation; n is n x 、n y For sample capacity; h is a x 、h y Is the bandwidth; k (K) x (·)、K y (. Cndot.) is the kernel function of the photovoltaic versus the load; t represents 24 time periods a day; f (F) T (x T ,y T ) Representing a joint probability distribution function of the T-period distributed photovoltaic and the load;
representing an edge distribution function of the distributed photovoltaic output in a period T;
Representing the edge distribution function of the load power over the period T.
4. The method for evaluating reliability of a power distribution network based on optical-load correlation enhancement reliability according to claim 3, wherein in step S3: generating an optical-charge typical scene considering correlation according to the optical-charge joint probability distribution function established in the step S2, wherein the optical-charge typical scene specifically comprises the following steps:
generating 24 random numbers u with values ranging from 0 to 1 based on Monte Carlo sampling idea 1 ，u 2 ，…，u n Namely, the edge distribution function value of the photovoltaic power in 24 time periods is obtained, and the edge distribution function v of the load output in 24 time periods is solved according to the joint probability distribution function of the photovoltaic power and the load in 24 time periods calculated by adopting the Copula function in the step S3 1 ，v 2 ，…，v n The operation is repeated for K times to generate K groups of edge distribution function values of the photovoltaic-load, then real output values of the photovoltaic-load considering time sequence correlation are obtained through respective inverse function calculation, then the scene generated by K groups of Monte Carlo sampling is clustered based on a fuzzy C-means clustering algorithm, and the clustering is performed according to the central category of the initialized clustering, the fuzzy parameters of the given membership factor, the initialized membership matrix and the given iteration terminationThe method comprises the steps of standard calculation, clustering center calculation, membership matrix updating, judging whether the termination standard is reached, and clustering result output, and finally k typical photovoltaic-load output day scenes can be obtained, wherein the mathematical expression is as follows:
wherein, C (&) represents a joint probability distribution function of photovoltaic and load; t represents 24 hours a day; u (u) T An edge distribution function value representing a photovoltaic power T period; v T An edge distribution function value representing a load power T period; x is x T Representing the true output value of the photovoltaic at the moment T; y is T Representing the real power value of the load at the moment T;
an inverse function representing an edge distribution function of the photovoltaic power over a period T;
An inverse function representing an edge distribution function of the load power over a period T;
Is an FCM cluster center; i represents the i-th sample; n is the number of samples; j represents a j-th cluster center; k is the number of clustering centers; t is the number of iterations; u (u) ij Membership of the ith sample to the jth cluster center; x is x i Is a photovoltaic and load sample point; m is a membership factor parameter; d, d ij Is the Euclidean distance of the ith sample to the center of the jth cluster.
5. The method for evaluating reliability of a power distribution network based on optical-load correlation enhancement reliability according to claim 4, wherein in step S4: and constructing a reliability evaluation model of the whole power distribution network through reliability modeling of the photovoltaic element, reliability modeling of the energy storage element, reliability evaluation index selection of the power distribution network and reliability method research of the power distribution network.
6. The method for evaluating the reliability of a power distribution network based on the enhanced reliability of the light-load correlation according to claim 5, wherein the reliability output of the photovoltaic element can be directly calculated by combining the distributed photovoltaic and load typical scene curve generated in the step S3 with the operation state of the photovoltaic equipment, and the reliability modeling mathematical expression of the photovoltaic element is as follows:
P T′ ＝P T *PVOS t
wherein: p (P) T' Representing a distributed photovoltaic element reliability output value at time t after combining a power curve of the distributed photovoltaic with a reliable operating state of the distributed photovoltaic component element; p (P) T The distributed photovoltaic output value is read by the distributed photovoltaic and load typical scene curve generated in the step S3; PVOS (PVOS) t Representing the reliability comprehensive operation state of the distributed photovoltaic component element at the time t;
the reliability modeling mathematical expression of the energy storage element is as follows:
SOCmin≤SOC(t)≤SOCmax
SOC(0)＝SOC(T)
wherein: SOC (t) is the stored energy state of charge at the current moment; SOC (State of Charge) max 、SOC min The upper limit and the lower limit of the energy storage charge state are respectively; η (eta) cha 、η dis The energy conversion efficiency during the charge and discharge of the stored energy is respectively; BOS (t) represents the running state of the energy storage device at the moment t; Δt takes one hour; x is x dis 、x cha The method comprises the steps of restraining the charging state and the discharging state of an energy storage device to be not carried out simultaneously for the charging and discharging states of the energy storage device; p (P) max And Q is equal to max Respectively representing the rated power and rated capacity of the energy storage system.
7. The method for evaluating the reliability of a power distribution network based on the light-load correlation enhancement reliability according to claim 6, wherein the power distribution network reliability evaluation index selects an average power supply availability of a system, an expected value of insufficient electric quantity and an average supplied electric quantity in an island state to evaluate the reliability of the whole power distribution network and verify the reliability of the power distribution network reliability evaluation based on the light-load time sequence correlation generation of distributed photovoltaic and load scenes, and the mathematical expression is as follows:
wherein:
wherein: ASAI represents the average power availability of the system; EENS represents a low power desired value; IIES represents the average supply power R in the island state as the system load point set; u (U) i The duration of the annual fault for the load point; n (N) i The number of users at the load point i;
user average power representing load point i;
Representing the distributed photovoltaic average power accessed by the load point i;
Representing the average power of the energy storage device accessed by the load point i; n represents the total number of island formations in a power distribution network in one year; n represents the formation of island for the nth time; r is R j Representing an island operation range formed for the nth time; Δt takes 1 hour; t is t n Indicating the island run time of the nth formation.
8. The method for evaluating reliability of a power distribution network based on optical-load correlation enhancement reliability according to claim 7, wherein in step S5: based on the light-load typical scene in the step S3 and the reliability evaluation model of the power distribution network in the step S4, researching a reliability operation strategy after the distributed photovoltaic and energy storage are accessed into the power distribution network to evaluate the reliability of the power distribution network, wherein the reliability operation strategy is specifically as follows:
the energy storage device operation strategy in the reliability operation strategy operates in a mode of supplying power to the load according to the island operation mode at normal time 'low storage and high release', and the fault time, in the normal operation mode, the energy storage device is discharged when the electricity price is at the highest value, the energy storage device is charged when the electricity price is at the lowest value, the SOC operation curve of the energy storage system in the whole day is simulated by the operation strategy without charging and discharging when the price is moderate, and the maximum SOC of the state of charge of the energy storage device is assumed to be 6 am every day max Point 22 is the state of charge minimum value SOC of the energy storage device min The energy storage device is charged and discharged uniformly in the running process, and the piecewise function of the SOC of the energy storage system at each moment can be obtained as follows:
after the SOC curve of the energy storage system in normal operation is obtained, the probability corresponding to each SOC stage can be calculated, and when the power distribution network fails, the SOC value of the energy storage system at the failure moment can be obtained by sampling by the formula;
in view of the characteristic that a fault influence range in the power distribution network takes a switch as a boundary, the power distribution network fault partition strategy in the reliability operation strategy calculates reliability by adopting a method of partitioning the power distribution network by taking the switch as a boundary, the reliability of load points in the partition is affected by faults of different elements outside the partition to the same extent, thus the calculated amount of a sampling method can be simplified, when the power distribution network element breaks down, the whole feeder line is divided into a fault area, a fault upstream area, a fault downstream area and a fault non-affected area by adopting a fault partition strategy, and the power failure time of the load points of the fault area is the fault repair time of the element; the fault can be isolated through the switch, and the power failure time of the load point of the upstream region of the fault is the switching time of the switch; if the energy storage device is used as a backup power supply in the fault downstream area, calculating the power failure time according to the energy storage configuration and the island operation strategy of the energy storage system; if the energy storage device is not used as a backup power supply, the power failure time of a load point of a downstream area is the repair time of a fault element;
the island operation strategy in the reliability operation strategy takes energy storage and distributed photovoltaic as centers, and the island formation size is judged by combining four factors of an energy storage SOC sampling value, energy storage maximum charge and discharge power, distributed photovoltaic output and total load power in an island range in a fault period; if the energy storage device and the distributed photovoltaic device can ensure that the current island runs smoothly, the island range is enlarged to the periphery by taking the switch as a boundary until the island with the maximum range is formed, and when the combined output of the distributed photovoltaic device and the energy storage device is insufficient during the island duration, the load needs to be reduced, and the mathematical expression is as follows:
the constraint conditions are as follows:
wherein: x (m) is a criterion of whether an mth load point in the island is cut down, wherein the load point m is equal to 1 when the load point m is cut down, and is equal to 0 after the load point m is cut down; p (P) i dmax (t) is the maximum output value of the ith distributed energy storage device in the island at the moment t, N b The number of the distributed energy storage devices in the island; p (P) j DG (t) is the power value of the jth distributed photovoltaic in the island at the moment t, N DG The number of distributed photovoltaic devices in the island; l (L) t m (t) is the actual power at the mth load point in the island; m is the number of load points contained within the island; t is t 1 、t 2 The forming time of the island and the ending time of the island in the power distribution network fault period are respectively.
9. The method for evaluating reliability of a power distribution network based on light-load correlation enhancement reliability according to any one of claims 1 to 8, wherein in step S5: based on the light-load typical scene in the step S3 and the reliability evaluation model of the power distribution network in the step S4, the reliability evaluation of the power distribution network is carried out by researching a reliability operation strategy after the distributed photovoltaic and energy storage are accessed into the power distribution network, wherein the reliability evaluation flow comprises the following steps:
step1: reading in system original data and setting a simulation time axis;
step2: generating N0-1 random numbers, sampling states of all elements according to a sequential Monte Carlo simulation method, and calculating a fault-free running time TTF;
step3: obtaining a fault element corresponding to the minimum TTF, and calculating the fault repair time TTR according to the fault repair rate of the element;
step4: determining the island operation range based on the light-load typical scene generated in the step S3 and the power distribution network reliability evaluation model in the step S4;
step5: recording information such as power failure times, time and corresponding load power at power failure time of each load point in the power distribution network;
step6: updating a time axis, judging whether the set simulation time length is reached, and if the set simulation time length is not reached, jumping to Step2;
step7: and calculating the reliability index of the load point and the system by using the recorded information such as the power failure times, time and corresponding load power of the power failure time of the load point.
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